Giovanna Abreu Alves , Roberto Tavares , Pedro Amorim , Victor Claudio Bento Camargo
{"title":"A systematic review of mathematical programming models and solution approaches for the textile supply chain","authors":"Giovanna Abreu Alves , Roberto Tavares , Pedro Amorim , Victor Claudio Bento Camargo","doi":"10.1016/j.cie.2025.110937","DOIUrl":"10.1016/j.cie.2025.110937","url":null,"abstract":"<div><div>The textile industry is a complex and dynamic system where structured decision-making processes are essential for efficient supply chain management. In this context, mathematical programming models offer a powerful tool for modeling and optimizing the textile supply chain. This systematic review explores the application of mathematical programming models, including linear programming, nonlinear programming, stochastic programming, robust optimization, fuzzy programming, and multi-objective programming, in optimizing the textile supply chain. The review categorizes and analyzes 163 studies across the textile manufacturing stages, from fiber production to integrated supply chains. Key results reveal the utility of these models in solving a wide range of decision-making problems, such as blending fibers, production planning, scheduling orders, cutting patterns, transportation optimization, network design, and supplier selection, considering the challenges found in the textile sector. Analyzing those models, we point out that sustainability considerations, such as environmental and social aspects, remain underexplored and present significant opportunities for future research. In addition, this study emphasizes the importance of incorporating multi-objective approaches and addressing uncertainties in decision-making to advance sustainable and efficient textile supply chain management.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"202 ","pages":"Article 110937"},"PeriodicalIF":6.7,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143420288","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Cesar Salazar-Santander, Alejandro F. Mac Cawley, Carolina Martinez-Troncoso
{"title":"An optimal effectiveness-driven target segment selection modeling approach for marketing campaign management","authors":"Cesar Salazar-Santander, Alejandro F. Mac Cawley, Carolina Martinez-Troncoso","doi":"10.1016/j.cie.2025.110945","DOIUrl":"10.1016/j.cie.2025.110945","url":null,"abstract":"<div><div>Defining a target group for a mass marketing campaign is a non-trivial goal, which depends on the correct definition of the commercial stimuli and the selection of a customer target segment that will maximize the campaign’s effectiveness. This process requires the analysis of multiple customer variables and interactions. The problem becomes even more complex if we consider a limited budget for the campaign. This research proposes a methodology based on a mixed multi-objective optimization formulation that allows us to determine a minimum continuous customer target segment for massive campaigns to maximize its effectiveness with a maximum budget constraint. The multi-objective function of the model maximizes the effectiveness of the campaign while minimizing the “broadness” of the targeted segments, allowing the detection of the most effective and homogeneous target group possible for a commercial action within a set of <span><math><mi>N</mi></math></span> continuous variables. The methodology performance was benchmarked against traditional customer clustering and greedy segmentation algorithms. The experiments were carried out in (1) simulated data environments and (2) based on real campaign information. The compared scenarios show that the proposed methodology outperforms the baseline model, the complexity of the problem scales non-linearly, increasing the number of variables, and the model increases 54% the effectiveness of a campaign without an increment in the segment range.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"202 ","pages":"Article 110945"},"PeriodicalIF":6.7,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143420291","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Small and medium-sized enterprise dedicated knowledge exploitation mechanism: A recommender system based on knowledge relatedness","authors":"Xingyu Sima , Thierry Coudert , Laurent Geneste , Aymeric de Valroger","doi":"10.1016/j.cie.2025.110941","DOIUrl":"10.1016/j.cie.2025.110941","url":null,"abstract":"<div><div>Knowledge is a vital asset for organizations, especially in today’s Industry 4.0 context with the ever-increasing amount of information being produced. Organizations must consider knowledge management (KM) to create a sustainable competitive advantage. Currently, KM is applied relatively well in large organizations; however, small and medium-sized enterprises (SMEs) encounter various constraints. Knowledge exploitation is a key phase in KM for the retrieval of relevant knowledge. Therefore, a recommender system (RS), which is a promising and widely used information technology (IT) tool, is proposed in this study, for SMEs to enable effective knowledge exploitation. The RS can be adapted to SME KM specificities and a dedicated RS based on knowledge relatedness derived from different information sources is proposed herein. The proposed RS enables the recommendation of knowledge item balancing: i) historical application data, that is, information regarding how items were related during past projects, and ii) initial relatedness knowledge, which represents the relationships between knowledge items defined by knowledge experts. The proposed RS was developed in collaboration with the Axsens-bte SME, who specialize in consultancy and training in the supply chain, Industry 4.0, and quality requirements management. The proposed RS improved SME KM processes and increased efficiency in terms of exploiting knowledge assets. This demonstrated the ability of the proposed RS to assist SMEs in efficiently and effectively navigating complex information environments.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"202 ","pages":"Article 110941"},"PeriodicalIF":6.7,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143427947","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Economic-emission dispatch problem in a biomass-coal co-firing CCHP system based on natural gas deep peak-shaving and carbon capture technologies","authors":"Jinliang Zhang, Zeping Hu","doi":"10.1016/j.cie.2025.110953","DOIUrl":"10.1016/j.cie.2025.110953","url":null,"abstract":"<div><div>Driven by the carbon peaking and carbon neutrality goals, the multi-energy coupling relationship of the combined cooling heat and power (CCHP) system is more complex than before, and the green and low-carbon transformation of energy needs to be promoted. Hence, this paper proposes a biomass-coal co-firing CCHP system based on natural gas deep peak-shaving and carbon capture technologies. Firstly, a deep peak-shaving model of the hydrogen gas turbine is established. Furthermore, the impact of deep peak-shaving cost allocation and compensation mechanism on the system is analyzed. Secondly, a biomass-coal co-firing model with carbon capture is constructed to reduce the carbon emission of the system. Thirdly, a heat-electric-cold demand coupling response model is set to accurately describe the coupled response relationship of various types of loads in response to an increase in the price of electricity. Finally, a low-carbon economic dispatch model of a biomass-coal co-firing CCHP system based on gas deep peak-shaving and carbon capture is constructed to minimize the total system operation cost, and the effectiveness of the model is verified by arithmetic examples. The results show that the proposed strategy can improve the economy, low carbon, and energy efficiency of the co-dispatch of the integrated energy system (IES).</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"203 ","pages":"Article 110953"},"PeriodicalIF":6.7,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143487666","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Habib Heydari , Mohammad Mahdi Paydar , Iraj Mahdavi
{"title":"A novel hybrid approach for designing green robust manufacturing cells","authors":"Habib Heydari , Mohammad Mahdi Paydar , Iraj Mahdavi","doi":"10.1016/j.cie.2025.110946","DOIUrl":"10.1016/j.cie.2025.110946","url":null,"abstract":"<div><div>People in today’s world pay more attention to environmental issues. This positive sensitivity which is accompanied by governmental regulations forces manufacturers to take account of cleaner design attributes in production system configurations. Companies face challenges in increasing flexibility and efficiency as preliminaries of production evolution while considering green components. An integrated framework in the context of green production is a practical solution. This paper introduces a comprehensive bi-objective model for designing green robust cellular manufacturing. Besides the minimization of cell formation, layout design, and production planning costs, an environmental objective is formulated in terms of the minimization of production wastes. Simple augmented ε-constraint (SAUGMECON) to obtain trade-off solutions in small scales of the model is applied. An illustrative example is provided to detail the SAUGMECON and the model characteristics. Analyses of the<!--> <!-->objective<!--> <!-->functions’<!--> <!-->weights in a fuzzy decision-making framework are also performed. For larger examples, a hybrid optimizer based on branch-and-bound, genetic, and SAUGMECON algorithms is developed. It is concluded that our hybrid multi-objective strategy yields very outstanding results.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"201 ","pages":"Article 110946"},"PeriodicalIF":6.7,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143378092","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Exact solution of workload consistent vehicle routing problem with priority distribution and demand uncertainty","authors":"Shiping Wu, Chun Jin, Hongguang Bo","doi":"10.1016/j.cie.2025.110940","DOIUrl":"10.1016/j.cie.2025.110940","url":null,"abstract":"<div><div>This study attempts to solve a workload consistent vehicle routing problem with priority distribution and demand uncertainty. Workload consistency requires the difference in working time allocated to drivers each day within a planning horizon to be limited to a fixed range. Partial split delivery, multi-trips, and uncertain demand are also considered. To address both transportation costs and priority-based distribution concerns, hierarchical objectives are adopted with the primary objective of minimizing travel costs and the secondary objective of maximizing distribution rewards. An exact algorithm based on set-partitioning formulation and robust column-and-cut generation is proposed to solve the problem, where a lower bound and an upper bound are used to derive some feasible columns, and these candidate columns are used in solving the set-partitioning formulation to obtain the optimal solution. Simultaneous decisions on visit sequence and distribution amount under conditions of demand uncertainty exacerbate the difficulty of solving the pricing subproblem. Therefore, we design a robust labelling algorithm involving a robust feasible extension check and an optimal distribution pattern computation to address this difficulty. The upper bound is obtained by a clustering-routing-assignment heuristics. Numerical experiments indicate that the proposed exact method can effectively solve medium-and partially large-scale instances, and the results have good robustness.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"202 ","pages":"Article 110940"},"PeriodicalIF":6.7,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143403064","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A framework of risk response strategy selection considering the loss caused by risk propagation in the project portfolio","authors":"Zhong Shen , Xingmei Li , Dongqing Jia , Xiaoyan Lv","doi":"10.1016/j.cie.2025.110935","DOIUrl":"10.1016/j.cie.2025.110935","url":null,"abstract":"<div><div>The successful implementation of project portfolio calls for effective risk management, in which how to select appropriate strategies to respond to upcoming risks, is called risk response strategy (RRS) selection. However, two deficiencies are discovered in current RRS selection research. The one is that researchers ignore the phenomenon of risk propagation and the risk losses it brings, which makes some risks with propagation potential underestimated in the process of risk evaluation. The other is that lacking approach to quantify the loss caused by risk propagation, which is not conducive to assessing risks and selecting RRSs from a quantitative perspective. Under this circumstance, this paper firstly proposes a framework of RRS selection considering the loss caused by risk propagation in the project portfolio. In the proposed framework, Bayesian network and fuzzy theory are used to measuring the probability of risk propagation between projects. Subsequently, the propagation process of risks among projects is generated, based on which the probability and time point of loss caused by each risk are calculated. Finally, the losses caused by risk propagation before and after applying risk response strategies are quantified, and an RRS selection model to maximize the recovered risk loss is constructed. The analysis of a case study demonstrates that 1) with the increase of available funds used to respond to risks, the recovered risk loss by each unit of fund declines; 2) before the implementation of projects, the decision-maker should select the RRSs that can recover more direct risk losses; 3) when projects have been implemented, decision-maker needs to focus on the indirect losses caused by risk propagation and select RRSs that can block the propagation of risks in the project portfolio.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"202 ","pages":"Article 110935"},"PeriodicalIF":6.7,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143420289","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Calendar design for assignment of ongoing appointments: Modeling, analysis and application","authors":"Yossi Luzon , Yariv N. Marmor","doi":"10.1016/j.cie.2025.110931","DOIUrl":"10.1016/j.cie.2025.110931","url":null,"abstract":"<div><div>Appointment booking (AB) is a widely used technique for managing elective services like hospital surgical units, law courtrooms, and other services with high demand and limited resources. AB typically assigns services to specific time slots using an appointment calendar. However, devising an effective AB policy is challenging due to varying service types, durations, and demand variability. In this study, we present a preplanned appointment calendar (PAC), designed using a two-stage stochastic programming model to tackle service scheduling challenges based on demand and historical data. The design of the PAC is generated offline, prior to the arrival of any customers requesting an appointment, and aims to minimize the time from the initial appointment request to service completion, namely, the patient’s sojourn time. Despite the substantial public expenses incurred when appointments are scheduled far in the future, encompassing both indirect costs (e.g., those related to the development of chronic diseases) and direct costs (e.g., those arising from employee absence), the sojourn-time measure has not received sufficient attention in the existing literature. Our method minimizes patients’ sojourn time while considering operational constraints and quality of service (QoS) considerations, resulting in a practical and user-friendly appointment booking system. Our approach is adjustable and easy to apply in real time. We introduce the chained-PAC (CPAC) mechanism, in which multiple, smaller PACs are joined together, and demonstrate the applicability of this approach by implementing it in a cardiac surgical operating room at a major hospital in Toronto, Canada. Results show the PAC approach reduces wait times and improves resource utilization in the surgical unit compared to the existing AB system. Our approach benefits healthcare providers and patients and can extend to other similar service systems.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"201 ","pages":"Article 110931"},"PeriodicalIF":6.7,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143334169","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jinjin Tang , Qianwang Deng , Changwen Wang , Mengqi Liao , Weifeng Han
{"title":"Integrated optimization of maintenance, spare parts management and operation for a multi-component system: A case study","authors":"Jinjin Tang , Qianwang Deng , Changwen Wang , Mengqi Liao , Weifeng Han","doi":"10.1016/j.cie.2025.110942","DOIUrl":"10.1016/j.cie.2025.110942","url":null,"abstract":"<div><div>Efficient maintenance activities are essential for the safe operation of industrial systems, and rational spare parts management, as an integral support to maintenance activities, is also closely linked to operation planning. In this paper, an integrated optimization model of maintenance, spare parts management, and operation for a single-machine multi-component system is proposed, shortened to MSO-SMPS. The goal of MSO-SMPS is the rational design of maintenance strategy, supported by an excellent collaborative management mechanism for new and used spare parts, achieving simultaneous optimization of the total cost and the completion time. Specifically, an adaptive opportunistic maintenance (OM) strategy and a reuse mechanism of retired components are designed to cope with dynamic changes in the system state and operating environment. Combining new and used spare parts can significantly improve the utilization of spare parts while ensuring that maintenance activities are carried out efficiently. In addition, to better address MSO-SMPS, an improved memetic algorithm (IMA) is proposed, in which an initialization method and four local search operators are designed to improve the solve efficiency. Finally, taking the tunnel boring machine (TBM) cutterhead system as a case, extensive experiments verify the effectiveness of the proposed designs.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"202 ","pages":"Article 110942"},"PeriodicalIF":6.7,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143395439","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A feature based neural network model for distributed flexible flow shop scheduling considering worker and transportation factors","authors":"Tianpeng Xu, Fuqing Zhao, Jianlin Zhang, Jianxin Tang, Hao Zhou","doi":"10.1016/j.cie.2025.110917","DOIUrl":"10.1016/j.cie.2025.110917","url":null,"abstract":"<div><div>In the context of distributed flexible flow shop scheduling (DFFSP), the factors of worker and transportation have a significant impact on the production efficiency within a manufacturing environment. However, previous research rarely considers both worker and transportation simultaneously. Therefore, this paper investigates a DFFSP with worker and transportation factors (DFFSP-WT). Considering the characteristic of DFFSP-WT, a neural network-based monarch butterfly optimization (NNMBO) is designed to minimize the objectives of makespan, total cost, and worker fatigue. In the NNMBO, the monarch butterfly optimization (MBO) is employed as the primary optimization operator to determine the job sequence. Furthermore, a feature-based search strategy (FSS), which encompasses six distinct local search operators, is developed to enhance the search capability. Additionally, a feature-based neural network model (FNN) is designed to adaptively select the best FSS. To verify the effectiveness of NNMBO, the simulation experiments are conducted with other state-of-the-art algorithms on test instances, the experimental results demonstrate that the NNMBO is a promising algorithm to solve DFFSP-WT.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"201 ","pages":"Article 110917"},"PeriodicalIF":6.7,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143377064","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}